Apparatus and method for detecting lesion and lesion diagnosis apparatus

ABSTRACT

An apparatus for detecting a lesion is provided. The apparatus includes an extracting unit configured to extract at least one tissue region from an image of tissue regions, a setting unit configured to set at least one of the at least one extracted tissue region as a lesion detection candidate region, and a detecting unit configured to detect a lesion from the lesion detection candidate region.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119(a) of KoreanPatent Application No. 10-2011-0073398, filed on Jul. 25, 2011, theentire disclosure of which is incorporated by reference for allpurposes.

BACKGROUND

1. Field

The following description relates to an apparatus and a method fordetecting lesions and a lesion diagnosis apparatus.

2. Description of the Related Art

With the advancement of surgery techniques, different kinds of minimuminvasive surgeries have been developed. A minimum invasive surgeryprocess represents a surgery method in which a medical operation may beperformed by approaching a lesion using surgical instruments withoutincising skin and muscle tissues. The surgical instruments may include asyringe or a catheter. The medical operation may include a medicineinjection, removal of lesions, appliance insertion etc. In order toperform the minimum invasive surgery process, doctors needs to locatethe lesion. Also, in order to diagnose a disease, the doctors may needto determine the size, shape and location of the lesion.

Various medical imaging equipment have been developed that can aid inthe detection of the size, shape and location of the lesion. Thesemedical imaging equipment include a Computed Tomography (CT) system, aMagnetic Resonance Imaging (MRI) system, a Positron Emission Tomography(PET) system, a Single Photon Emission Computed Tomography (SPECT), etc.

However, it may be difficult to precisely extract a lesion since theimages produced by theses medical imaging equipment are typically ofpoor quality. Accordingly, a need exists for a technology capable ofprecisely extracting a lesion.

SUMMARY

According to an aspect, an apparatus for detecting a lesion is provided.The apparatus includes an extracting unit configured to extract at leastone tissue region from an image of tissue regions, a setting unitconfigured to set at least one of the at least one extracted tissueregion as a lesion detection candidate region, and a detecting unitconfigured to detect a lesion from the lesion detection candidateregion.

The extracting unit may extract the at least one tissue region from theimage by comparing feature information of the image to featureinformation of the tissue regions.

The extracting unit may partition the image into a plurality of regionsby performing an image segmentation on the image and compare featureinformation of the partitioned region with feature information of thetissue region to extracts the at least one tissue region from the image.

Each of the feature information of the image and the feature informationof the tissue regions may be represented by brightness, color, texture,relative location and shape, or any combination thereof.

The image may correspond to an image of a breast, and the lesiondetection candidate region corresponds to a mammary glandular tissueregion.

The extracting unit may compare feature information of the image of thebreast with feature information of the mammary glandular tissue regionto extract the mammary glandular tissue region from the image of thebreast.

The extracting unit may extract breast tissue regions from the image ofthe breast by partitioning the image of the breast into a plurality ofregions by performing an image segmentation on the image of the breastand comparing feature information of the partitioned region with featureinformation of the tissue regions.

The extracting unit may extract a subcutaneous fat tissue region and apectoralis muscle region and extract a region between the subcutaneousfat tissue region and the pectoralis muscle region as the mammaryglandular tissue region.

Breast tissue regions of the image of the breast may include asubcutaneous fat tissue region, the mammary glandular tissue region anda pectoralis muscle region.

Feature information of the subcutaneous fat tissue region may indicatean upper location of the image, a darker brightness compared to otherregions and a round shape, feature information of the pectoralis muscleregion may indicate a lower location of the image and a band-liketexture having a uniform direction, and feature information of themammary glandular tissue region may indicate a location betweensubcutaneous fat tissue region and the pectroalis muscle region and asmall spot like texture.

The extracting unit may perform a morphology operation to remove noiseincluded in an image corresponding to the extracted tissue region.

The extracting unit may include a gabor filter, a spatial gray leveldependence (SGLD) filter, or a wavelet filter.

The extracting unit may utilize a window having a size of N*N or N*M toextract feature information of the image.

In another aspect, a method for detecting a lesion is provided. Themethod includes extracting at least one tissue region from an image oftissue regions, setting at least one of the at least one extractedtissue region as a lesion detection candidate region, and detecting alesion from the lesion detection candidate region.

The extracting of the at least one tissue region from the image mayinclude comparing feature information of the image with featureinformation of the tissue regions.

The extracting of the at least one tissue region from the image mayinclude performing an image segmentation on the image to partition theimage into a plurality of regions, and comparing feature information ofthe partitioned region with feature information of the tissue regions.

The image may be an image of a breast, and the lesion detectioncandidate region may be a mammary glandular tissue region.

The extracting of the at least one tissue region from the image mayinclude comparing feature information of the image of the breast withfeature information of the mammary glandular tissue region to extractonly the mammary glandular tissue region from the image of the breast.

The extracting of the at least one tissue region from the image includesextracting breast tissue regions from the image of the breast, theextracting breast tissue regions from the image of the breast mayinclude performing an image segmentation on the image of the breast topartition the image of the breast into a plurality of regions, andcomparing feature information of the partitioned region with featureinformation of the mammary glandular tissue region.

The extracting of the at least one tissue region from the image includesextracting a subcutaneous fat tissue region and a pectoralis muscleregion from the image of the breast, and extracting a region between thesubcutaneous fat tissue region and the pectoralis muscle region as themammary glandular tissue region.

In another aspect, an apparatus for diagnosing a lesion is provided. Theapparatus includes an image acquisition unit configured to photograph aninside of an organism to acquire an image, an lesion detecting unitincluding an extracting unit configured to extract at least one tissueregion from the image, a setting unit configured to set at least one ofthe at least one extracted tissue region as a lesion detection candidateregion and a detecting unit configured to detect a lesion from thelesion detection candidate region, and a lesion diagnosis unitconfigured to diagnose a name of disease based on the lesion detected bythe lesion detecting unit.

The image may be an image of a breast, and the lesion detectioncandidate region is a mammary glandular tissue region.

In another aspect, a medical imaging device for detecting a lesion isprovided. The device may include an extracting unit configured to set atleast one tissue region, as a lesion detection candidate region, from animage of a plurality of tissue regions, and a detecting unit configuredto detect a lesion from the lesion detection candidate region.

Other features and aspects may be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a lesion detectingapparatus.

FIG. 2 is a diagram illustrating an example of a lesion diagnosisapparatus.

FIGS. 3A to 3D are diagrams illustrating an example of a process fordetecting a lesion by the lesion detecting apparatus of FIG. 1.

FIGS. 4A to 4E are diagrams illustrating another example of a processfor detecting a lesion by the lesion detecting apparatus of FIG. 1.

FIGS. 5A to 5D are diagrams illustrating yet another example of aprocess for detecting a lesion by the lesion detecting apparatus of FIG.1.

FIG. 6 is a diagram illustrating an example of a method for detecting alesion.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. Accordingly, various changes,modifications, and equivalents of the systems, apparatuses and/ormethods described herein will be suggested to those of ordinary skill inthe art. Also, descriptions of well-known functions and constructionsmay be omitted for increased clarity and conciseness.

Hereafter, examples will be described with reference to accompanyingdrawings.

FIG. 1 illustrates an example of a lesion detecting apparatus.

Referring to FIG. 1, a lesion detecting apparatus 100 includes anextracting unit 110, a setting unit 120 and a detecting unit 130.

The extracting unit 110 may extract at least one tissue region from animage. The image may capture the inside of an organism and the image mayinclude a plurality of tissue regions.

For example, the extracting unit 110 may use a window having a size ofN*N or N*M to extract feature information of the image. The extractingunit 110 may compare feature information of the image with featureinformation of the tissue regions to extract the tissue region from theimage. As an example, the extraction unit 110 may use a machine learningto extract the tissue region from the image. The machine learning mayinclude a Support Vector Machine (SVM), a Random Forest (RF)classification, etc. The feature information of the image and thefeature information of the tissue region may represent brightness,color, texture, relative location, shape, or any combination thereof.The extracting unit 110 may use a gabor filter, a spatial gray leveldependence (SGLD) filter or a wavelet filter to extract texture featureinformation.

As another example, the extracting unit 110 may perform imagesegmentation on the image to partition the image into a plurality ofregions. The extracting unit 110 may extract feature information foreach partitioned region. The extracting unit 110 may compare the featureinformation of the partitioned region with feature information of thetissue region to determine the tissue region of the image.

Hereinafter, the description will be further made assuming that theimage is an image of breasts and that a user or a manufacturerdetermines a mammary glandular tissue region as a lesion detectionobject region.

As another example, the extracting unit 110 may compare featureinformation of the image of the breasts with feature information of themammary glandular tissue region. Subsequently, the extracting unit 110may extract only the mammary glandular tissue region from the image ofthe breasts.

As yet another example, the extracting unit 110 may compare featureinformation of the image of the breast with feature information of thebreast tissue region. Subsequently, the extracting unit 110 may extracta breast tissue region from the image of the breast. The breast tissueregion includes a subcutaneous fat tissue region, the mammary glandulartissue region and a pectoralis muscle region. The subcutaneous fattissue region may have a darker brightness than the brightness of othertissue regions and have a round shape. The pectoralis muscle region mayhave a band-like texture with a uniform direction. The mammary glandulartissue region may have a spot-like texture and exist between thesubcutaneous fat tissue region and the pectoralis muscle region. Asdescribed above, the extracting unit 110 may extract only the mammaryglandular tissue region or extract all of the breast tissue regions fromthe image of the breast.

As yet another example, the extracting unit 110 may perform an imagesegmentation on the image of the breast to partition the image of thebreasts into a plurality of regions. The extracting unit 110 may extractfeature information from each partitioned region. The extracting unit110 may compare feature information of the partitioned region withfeature information of the mammary grandular tissue region to extract aregion. As described above, the extracting unit 110 may extract only themammary glandular tissue region or extract all of the breast tissueregions from the image.

As yet another example, the extracting unit 110 may extract thesubcutaneous fat tissue region and the pectoralis muscle region from theimage of the breast. The extracting unit 110 may designate as themammary glandular tissue region a region between the subcutaneous fattissue region and the pectoralis muscle region.

The extracting unit 110 may perform a morphology operation to removenoise included in an image related to the extracted tissue region.

The setting unit 120 may set as a lesion detection candidate region atleast one of the at least one extracted tissue region. For example, auser may specify a region having a higher chance of having a lesion thanother regions as the lesion detection candidate region. As anotherexample, in response to a user or a manufacturer specifying a cerebellumregion and a muscle region as the lesion detection candidate region, thesetting unit 120 may set the cerebellum region and the muscle region asthe lesion detection candidate region. The cerebellum region and themuscle region may be regions among the extracted tissue regions. Inresponse to the image being an image of a breast, a user or amanufacture may specify a mammary glandular tissue region as the lesiondetection candidate region. The mammary glandular tissue region may be aregion in which lesions more frequently occur than in other tissueregions. In this example, the setting unit 120 sets the mammaryglandular tissue region among the extracted tissue regions as the lesiondetection candidate region.

The detecting unit 130 may detect a lesion from the lesion detectioncandidate region. For example, the detection region 130 may detectinformation about the location, size and shape of the lesion. Thedetecting unit 130 may use an image segmentation scheme to detect alesion. The image segmentation scheme may include a binary histogramthresholding method (BHT), a super-pixel scheme, or the like.

As described above, the lesion detecting apparatus may extract tissueregions from an image, set at least one of the extracted tissue regionsas a lesion detection candidate region and perform detection only on thelesion detection candidate region. Accordingly, the chance of detectinga lesion may be increased and the time taken to detect a lesion may bereduced.

FIG. 2 illustrates an example of a lesion diagnosis apparatus.

Referring to FIG. 2, a lesion diagnosis apparatus 200 includes an imageacquisition unit 210, a lesion detecting unit 220 and a lesion diagnosisunit 230.

The image acquisition unit 210 may capture the inside of an organism asan image. The image may include a plurality of tissue regions. Forexample, the image acquisition unit 210 may be a ultrasonic imagingsystem, a Computed Tomography (CT) system, a Magnetic Resonance Imaging(MRI) system, or any other system capable of photographing the inside ofan organism.

The lesion detecting unit 220 may detect a lesion from the imagegenerated by the image acquisition unit 210. For example, the lesiondetecting unit 220 may extract information from the image about alesion. The extracted information may include the location, size andshape of the lesion. The lesion detecting unit 220 may be implementedusing a similar corresponding structure as that of the lesion detectingapparatus 100 shown in FIG. 1, and the lesion detecting unit 220 mayhave the same function as the lesion detecting apparatus 100. The lesiondetecting unit 220 may include an extracting unit, a setting unit and adetecting unit. The extracting unit may extract at least one tissueregions from an image. The setting unit may set at least one of theextracted tissue regions as a lesion detection candidate region. Thedetecting unit may detect a lesion from the lesion detecting candidateregion. The extracting unit, the setting unit and the detecting unit mayhave substantially the same structures as the corresponding units shownin FIG. 1 and detailed descriptions thereof will be omitted forconciseness.

The lesion diagnosis unit 230 may diagnose the name of disease based oninformation from the lesion detected by the lesion detecting unit 220.For example, the lesion diagnosis unit 230 may diagnose the name ofdisease based on the information of the lesion, such as location, sizeand shape of the lesion.

As described above, the lesion diagnosis apparatus 200 may segment animage into a plurality of tissue regions, extract tissue regions fromthe image, set at least one of the extracted tissue regions as a lesiondetection candidate region and perform detection only on the lesiondetection candidate region. Accordingly, the lesion may be automaticallydetected and the name of disease may be diagnosed rapidly.

FIGS. 3A to 3D illustrate an example of a process of detecting a lesionby the lesion detecting apparatus of FIG. 1.

Hereinafter, a process of detecting a lesion from an image of the innerpart of a breast produced by the lesion detecting apparatus will bedescribed. However, the disclosure is not limited thereto, and thelesion detecting apparatus may detect lesions in other parts of a bodyin addition to the breast.

FIG. 3A illustrates a side view of a breast.

Referring to FIGS. 1 and 3A, tissue regions of the breast include a skinregion 300, a fat tissue region 310, a mammary glandular tissue region320 and a pectoralis muscle region 330. The fat tissue region 310includes a subcutaneous fat tissue region 311 and a retromammary fatregion 312. Hereinafter, the following description is made on theassumption that an image of the breast is taken in the direction ofleader line 340.

Referring to FIGS. 1 and 3B, tissue regions of the breast include thesubcutaneous fat tissue region 311, the mammary glandular tissue region320, the retromammary fat region 312 and the pectoralis muscle region330. The mammary glandular tissue region 320 may include a lesion 321.The tissue regions of the breast may have feature information asfollows. Feature information of the subcutaneous fat tissue region 311and the retromammary fat tissue region 312 may indicate a darkerbrightness in comparison to other regions and a round shape. Featureinformation of the subcutaneous fat tissue region 311 may indicate anupper location of the image. Feature information of the mammaryglandular tissue region 320 may indicate that the mammary glandulartissue region 320 is located at a location between the fat tissue region310 and the pectoralis muscle region 330 and indicate a small spot liketexture. Feature information of the pectoralis muscle region 330 mayindicate a lower location of the image and indicate a band-like texturehaving a uniform direction.

Referring to FIGS. 1 and 3C, the lesion detecting apparatus 100 mayextract feature information of the image of the breast. For example, thelesion detecting apparatus 100 may use a window having a size of N*N orN*M to extract feature information of the image.

Hereinafter, the description will be made on the assumption that themammary glandular tissue region 320 is set as the lesion detectioncandidate region.

For example, the lesion detecting apparatus 100 may compare featureinformation of the image of the breast with feature information of thetissue regions of the breast to extract tissue regions. The extractedtissue regions may include the subcutaneous fat tissue region 311, theretromammary fat region 312, the mammary glandular tissue region 320 andthe pectoralis muscle region 330. The lesion detecting apparatus 100 mayperform a morphology operation to reduce noise in an image correspondingto the extracted tissue region. The lesion detecting apparatus 100 mayset the mammary glandular tissue region 320 as the lesion detectioncandidate region. The mammary glandular tissue region 320 may be one ofthe tissue regions among the extracted tissue regions 311, 312, 320 and330.

For example, the lesion detecting apparatus 100 may compare featureinformation of the image of the breast with feature information of themammary glandular tissue region 320 to extract the mammary glandulartissue region 320 from the image of the breast. Subsequently, the lesiondetecting apparatus 100 may set the mammary glandular tissue region 320as the lesion detection candidate region.

Referring to FIGS. 1 and 3D, the lesion detecting apparatus 100 maydetect a lesion 321 from the lesion detection candidate region 320. Forexample, the lesion detecting apparatus 100 may use an imagesegmentation scheme to detect the lesion. The image segmentation schememay include a binary histogram thresholding method (BHT), a super-pixelscheme, or the like.

As described above, the lesion detecting apparatus detects a lesion fromthe lesion detection candidate region having a higher chance of havinglesions than other tissue regions. Accordingly, there is little need toperform lesion detection on the other tissue regions having a lowerchance of having lesions. Accordingly, the time used to detect lesionsis reduced and the lesions are more precisely detected.

FIGS. 4A to 4E illustrate another example of a process of detecting alesion by the lesion detecting apparatus of FIG. 1.

Hereinafter, a process of detecting a lesion from an image of the innerpart of a breast taken by the image acquisition unit will be described.However, the disclosure is not limited thereto, and the lesion detectingapparatus may detect lesions of other parts of a body in addition to thebreast.

Referring to FIGS. 1 and 4A, tissue regions of the breast include thesubcutaneous fat tissue region 311, the mammary glandular tissue region320, the retromammary fat region 312 and the pectoralis muscle region330. The tissue regions of the breast may have feature information asfollows. Feature information of the subcutaneous fat tissue region 311and the retromammary fat tissue region 312 may indicate a darkerbrightness in comparison to other regions and a round shape. Featureinformation of the subcutaneous fat tissue region 311 may indicate anupper location of the image. Feature information of the mammaryglandular tissue region 320 may indicate a location between the fattissue region 310 and the pectoralis muscle region 330 and a small spotlike texture. Feature information of the pectoralis muscle region 330may indicate a lower location of the image and a band-like texturehaving a constant direction.

Referring to FIGS. 1 and 4B, the lesion detecting apparatus 100 mayperform an image segmentation on the image of the breast to partitionthe image into a plurality of regions.

Referring to FIGS. 1 and 4C, the lesion detecting apparatus 100 mayextract feature information from each partitioned region. The lesiondetecting apparatus 100 may compare feature information of onepartitioned region with feature information of one tissue region torecognize each of the partitioned regions corresponds to which tissueregion. The lesion detecting apparatus 100 may extract the tissueregions 300, 311, 312, 320 and 330 from the image based on therecognized result.

Referring to FIGS. 1 and 4D, the lesion detecting apparatus 100 may setthe mammary glandular tissue region 320 as the lesion detectioncandidate region from among the tissue regions 300, 311, 312, 320 and330.

Referring to FIGS. 1 and 4E, the lesion detecting apparatus 100 maydetect the lesion 321 from the lesion detection candidate region 320.For example, the lesion detecting apparatus 100 may use an imagesegmentation to detect the lesion. The image segmentation may include abinary histogram thresholding method (BHT), a super-pixel scheme, or thelike.

As described above, the lesion detecting apparatus may partition theimage of the breast into a plurality of regions and extract tissueregions of the image of the breast based on the feature information ofeach partitioned region, thereby improving the precision of extractingthe tissue regions.

In addition, the lesion detecting apparatus may not need to performlesion detection on tissue regions having a lower chance of havinglesions, so that the time taken to detect lesions is reduced and thelesions are more precisely detected.

FIGS. 5A to 5D illustrate another example of a process of detecting alesion by the lesion detecting apparatus of FIG. 1.

Hereinafter, a process of detecting a lesion from an image of the innerpart of a breast taken by the image acquisition unit will be described.However, the disclosure is not limited thereto, and the lesion detectingapparatus may detect lesions of other parts of a body in addition to thebreast.

Referring to FIGS. 1 and 5A, tissue regions of the breast include thesubcutaneous fat tissue region 311, the mammary glandular tissue region320, the retromammary fat region 312 and the pectoralis muscle region330. The mammary glandular tissue region 320 may include a lesion 321.The tissue regions of the breast have feature information as follows.Feature information of the subcutaneous fat tissue region 311 and theretromammary fat tissue region 312 may indicate a darker brightnesscompared to other regions and a round shape. Feature information of thesubcutaneous fat tissue region 311 may indicate an upper location of theimage. Feature information of the mammary glandular tissue region 320may indicate the location between the fat tissue region 310 and thepectoralis muscle region 330 and a small spot like texture. Featureinformation of the pectoralis muscle region 330 may indicate a lowerlocation of the image and a band-like texture having a constantdirection.

Referring to FIGS. 1 and 5B (a), the lesion detecting apparatus 100 mayextract the fat tissue region 310 including the subcutaneous fat tissueregion 311 and the retromammary fat region 312 from the image of thebreast.

Referring to FIGS. 1 and 5B (b), the lesion detecting apparatus 100 mayextract the pectoralis muscle region 330 from the image of the breast.

Referring to FIGS. 1 and 5C, the lesion detecting apparatus 100 mayextract a region between the fat tissue region 310 and the pectoralismuscle region 330 as the mammary glandular tissue region 320. The lesiondetecting apparatus 100 may set the extracted mammary glandular tissueregion 320 as the lesion detection candidate region.

Referring to FIGS. 1 and 5D, the lesion detecting apparatus 100 maydetect the lesion 321 from the lesion detecting candidate region 320.For example, the lesion detecting apparatus 100 may use of an imagesegmentation scheme to detect the lesion. For example, the imagesegmentation scheme may include a binary histogram thresholding method(BHT), a super-pixel scheme, or the like.

As described above, the lesion detecting apparatus may detect a lesionfrom the lesion detection candidate region having a higher chance ofhaving lesions than other tissue regions, so there is no need for thelesion detecting apparatus to perform lesion detection on the othertissue regions having a lower chance of having lesions. Accordingly, thetime used to detect lesions may be reduced and the lesions may be moreprecisely detected.

FIG. 6 illustrates an example of a method for detecting a lesion.

Referring to FIG. 6, the lesion detecting apparatus extracts at leastone tissue region from an image (600).

For example, the lesion detecting apparatus extracts tissue regions froman image by comparing feature information of the image with featureinformation of the tissue region.

The lesion detecting apparatus may partition an image into a pluralityof regions by performing an image segmentation on the image, comparefeature information of the partitioned region with feature informationof the plurality of tissue regions, thereby extracting the tissueregions.

In response to the image being of a breast, the lesion detectingapparatus may extract at least breast tissue region from the image ofthe breast. For example, the lesion detecting apparatus may comparefeature information of the image of the breast with feature informationof the breast tissue region to extract at least one breast tissue regionfrom the image of the breast.

For example, the lesion detecting apparatus may perform an imagesegmentation on the image of the breast to partition the image of thebreast into a plurality of regions and compare feature information ofthe partitioned plurality of regions with feature information of themammary glandular tissue region to extract the mammary glandular tissueregion.

For example, the lesion detecting apparatus may detect the subcutaneousfat tissue region and the pectoralis muscle region from the image of thebreasts and extract a region between the subcutaneous fat tissue regionand the pectoralis muscle region as the mammary glandular tissue region.

The lesion detecting apparatus may set at least one of the extractedtissue regions as a lesion detection candidate region (610). Forexample, in response to the image being an image of breasts, the lesiondetecting apparatus may set the mammary glandular tissue region amongthe breast tissue regions as the lesion detection candidate region.

The lesion detecting apparatus may detect lesions from the lesiondetection candidate region (620).

According to this example of the method for detecting lesion, at leastone tissue region is extracted from the image, some of the extractedtissue regions may be set as a lesion detection candidate region, andthe lesion detection is performed only on the lesion detection candidateregion. Accordingly, the chance of detecting a lesion is improved andthe time used to detect the lesion is reduced.

Program instructions to perform a method described herein, or one ormore operations thereof, may be recorded, stored, or fixed in one ormore computer-readable storage media. The program instructions may beimplemented by a computer. For example, the computer may cause aprocessor to execute the program instructions. The media may include,alone or in combination with the program instructions, data files, datastructures, and the like. Examples of computer-readable media includemagnetic media, such as hard disks, floppy disks, and magnetic tape;optical media such as CD ROM disks and DVDs; magneto-optical media, suchas optical disks; and hardware devices that are specially configured tostore and perform program instructions, such as read-only memory (ROM),random access memory (RAM), flash memory, and the like. Examples ofprogram instructions include machine code, such as produced by acompiler, and files containing higher level code that may be executed bythe computer using an interpreter. The program instructions, that is,software, may be distributed over network coupled computer systems sothat the software is stored and executed in a distributed fashion. Forexample, the software and data may be stored by one or more computerreadable recording mediums. Also, functional programs, codes, and codesegments for accomplishing the example embodiments disclosed herein canbe easily construed by programmers skilled in the art to which theembodiments pertain based on and using the flow diagrams and blockdiagrams of the figures and their corresponding descriptions as providedherein. Also, the described unit to perform an operation or a method maybe hardware, software, or some combination of hardware and software. Forexample, the unit may be a software package running on a computer or thecomputer on which that software is running.

A number of examples have been described above. Nevertheless, it will beunderstood that various modifications may be made. For example, suitableresults may be achieved if the described techniques are performed in adifferent order and/or if components in a described system,architecture, device, or circuit are combined in a different mannerand/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

1. An apparatus for detecting a lesion, the apparatus comprising: anextracting unit configured to extract at least one tissue region from animage of tissue regions; a setting unit configured to set at least oneof the at least one extracted tissue region as a lesion detectioncandidate region; and a detecting unit configured to detect a lesionfrom the lesion detection candidate region.
 2. The apparatus of claim 1,wherein the extracting unit extracts the at least one tissue region fromthe image by comparing feature information of the image to featureinformation of the tissue regions.
 3. The apparatus of claim 1, whereinthe extracting unit partitions the image into a plurality of regions byperforming an image segmentation on the image and compares featureinformation of the partitioned region with feature information of thetissue region to extracts the at least one tissue region from the image.4. The apparatus of claim 2, wherein each of the feature information ofthe image and the feature information of the tissue regions isrepresented by brightness, color, texture, relative location and shape,or any combination thereof.
 5. The apparatus of claim 1, wherein theimage corresponds to an image of a breast, and the lesion detectioncandidate region corresponds to a mammary glandular tissue region. 6.The apparatus of claim 5, wherein the extracting unit compares featureinformation of the image of the breast with feature information of themammary glandular tissue region to extract the mammary glandular tissueregion from the image of the breast.
 7. The apparatus of claim 5,wherein the extracting unit extracts breast tissue regions from theimage of the breast by partitioning the image of the breast into aplurality of regions by performing an image segmentation on the image ofthe breast and comparing feature information of the partitioned regionwith feature information of the tissue regions.
 8. The apparatus ofclaim 5, wherein the extracting unit extracts a subcutaneous fat tissueregion and a pectoralis muscle region and extracts a region between thesubcutaneous fat tissue region and the pectoralis muscle region as themammary glandular tissue region.
 9. The apparatus of claim 5, whereinbreast tissue regions of the image of the breast include a subcutaneousfat tissue region, the mammary glandular tissue region and a pectoralismuscle region.
 10. The apparatus of claim 9, wherein feature informationof the subcutaneous fat tissue region indicates an upper location of theimage, a darker brightness compared to other regions and a round shape,feature information of the pectoralis muscle region indicates a lowerlocation of the image and a band-like texture having a uniformdirection, and feature information of the mammary glandular tissueregion indicates a location between subcutaneous fat tissue region andthe pectroalis muscle region and a small spot like texture.
 11. Theapparatus of claim 1, wherein the extracting unit performing amorphology operation to remove noise included in an image correspondingto the extracted tissue region.
 12. A method for detecting a lesion, themethod comprising: extracting at least one tissue region from an imageof tissue regions; setting at least one of the at least one extractedtissue region as a lesion detection candidate region; and detecting alesion from the lesion detection candidate region.
 13. The method ofclaim 12, wherein the extracting of the at least one tissue region fromthe image includes comparing feature information of the image withfeature information of the tissue regions.
 14. The method of claim 12,wherein the extracting of the at least one tissue region from the imagecomprises: performing an image segmentation on the image to partitionthe image into a plurality of regions; and comparing feature informationof the partitioned region with feature information of the tissueregions.
 15. The method of claim 12, wherein the image is an image of abreast, and the lesion detection candidate region is a mammary glandulartissue region.
 16. The method of claim 15, wherein the extracting of theat least one tissue region from the image includes comparing featureinformation of the image of the breast with feature information of themammary glandular tissue region to extract only the mammary glandulartissue region from the image of the breast.
 17. The method of claim 15,wherein the extracting of the at least one tissue region from the imageincludes extracting breast tissue regions from the image of the breast,the extracting breast tissue regions from the image of the breastcomprises: performing an image segmentation on the image of the breastto partition the image of the breast into a plurality of regions; andcomparing feature information of the partitioned region with featureinformation of the mammary glandular tissue region.
 18. The method ofclaim 15, wherein the extracting of the at least one tissue region fromthe image comprises: extracting a subcutaneous fat tissue region and apectoralis muscle region from the image of the breast; and extracting aregion between the subcutaneous fat tissue region and the pectoralismuscle region as the mammary glandular tissue region.
 19. An apparatusfor diagnosing a lesion, the apparatus comprising: an image acquisitionunit configured to photograph an inside of an organism to acquire animage; an lesion detecting unit including an extracting unit configuredto extract at least one tissue region from the image, a setting unitconfigured to set at least one of the at least one extracted tissueregion as a lesion detection candidate region and a detecting unitconfigured to detect a lesion from the lesion detection candidateregion; and a lesion diagnosis unit configured to diagnose a name ofdisease based on the lesion detected by the lesion detecting unit. 20.The apparatus of claim 19, wherein the image is an image of a breast,and the lesion detection candidate region is a mammary glandular tissueregion.
 21. A medical imaging device for detecting a lesion, the devicecomprising: an extracting unit configured to set at least one tissueregion, as a lesion detection candidate region, from an image of aplurality of tissue regions; and a detecting unit configured to detect alesion from the lesion detection candidate region.
 22. The apparatus ofclaim 1, wherein the extracting unit includes a gabor filter, a spatialgray level dependence (SGLD) filter, or a wavelet filter.
 23. Theapparatus of claim 1, wherein the extracting unit utilizes a windowhaving a size of N*N or N*M to extract feature information of the image.